class_docstr: A compact implementation of the full Rainbow agent.
code: def _build_networks_and_optimizer(self):
      self._rng, rng = jax.random.split(self._rng)
      state = self.preprocess_fn(self.state)
      self.online_params = self.network_def.init(
          rng, x=state, support=self._support
      )
      self.optimizer = dqn_agent.create_optimizer(self._optimizer_name)
      self.optimizer_state = self.optimizer.init(self.online_params)
      self.target_network_params = self.online_params
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling the JaxFullRainbowAgent obj's underlying member method code with example usage: obj._build_networks_and_optimizer() without return types